{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "400bcb10-9062-4c66-9b66-987e325e919b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/KGAPT_Vol/anaconda3/envs/envmegrapt/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics \n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import pickle\n",
    "import glob\n",
    "import sys\n",
    "from statistics import mean\n",
    "def load_checkpoint(file_path):\n",
    "    with open(file_path, 'rb') as f:\n",
    "        data = torch.load(f)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b943f931-87c0-4210-8789-7fbc351e5f33",
   "metadata": {},
   "source": [
    "## Collecting Results\n",
    "- Poirot didn't report detailed scores of benign cases, they report only the highest score among all the benign datasets which is 0.16, therefore we used it as the score of all benign cases  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df1d50d4-b0c9-4af4-8fa8-fb6a630ba8b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = {} \n",
    "score = {}\n",
    "#cadets\n",
    "y_true[\"cadets\"] = [1,1,1,1,0,0,0,0]\n",
    "score[\"cadets\"] = {}\n",
    "score[\"cadets\"][\"megrapt\"] = [0.55,0.42,0.57,0.47,0.27,0.36,0,0.25]\n",
    "#Poirot didn't report detailed scores of benign cases, they report only the highest score among all the benign datasets which is 0.16, therefore we used it as the score of all benign cases  \n",
    "score[\"cadets\"][\"poirot\"] = [0.64,0.81,0.89,1,0.16,0.16,0.16,0.16]\n",
    "score[\"cadets\"][\"deephunter\"] = [0.511,0.51,0.512,0.513,0.514,0.511,0,0.511] \n",
    "\n",
    "#theia\n",
    "y_true[\"theia\"] = [1,1,0,0]\n",
    "score[\"theia\"] = {}\n",
    "score[\"theia\"][\"megrapt\"] = [0.92,0.59,0,0]\n",
    "score[\"theia\"][\"poirot\"] = [0.58,0.71,0.16,0.16]\n",
    "score[\"theia\"][\"deephunter\"] = [0.511,0.511,0,0] \n",
    "\n",
    "#trace\n",
    "y_true[\"trace\"] = [1,1,0,0]\n",
    "score[\"trace\"] = {}\n",
    "score[\"trace\"][\"megrapt\"] = [0.43,0.46,0.36,0.36]\n",
    "#Poirot didn't report detailed scores of benign cases, they report only the highest score among all the benign datasets which is 0.16, therefore we used it as the score of all benign cases  \n",
    "score[\"trace\"][\"poirot\"] = [0.54,0.87,0.16,0.16]\n",
    "score[\"trace\"][\"deephunter\"] = [0,0.52,0.517,0.52] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "503d76a2-b0fc-48ea-a7ad-2849a4e9ef80",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, threshold = metrics.roc_curve(y_true[dataset], score[dataset][model_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b1ed278e-b2e7-451b-90df-b6873bbceb40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'trace'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b71b45f-0928-4bf0-a32b-9fe3d583d625",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on cadets dataset\n",
      "AUC of megrapt is 1.0\n",
      "AUC of poirot is 1.0\n",
      "AUC of deephunter is 0.5625\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on theia dataset\n",
      "AUC of megrapt is 1.0\n",
      "AUC of poirot is 1.0\n",
      "AUC of deephunter is 1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on trace dataset\n",
      "AUC of megrapt is 1.0\n",
      "AUC of poirot is 1.0\n",
      "AUC of deephunter is 0.375\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dataset in score.keys():\n",
    "    print(\"Evaluation on\",dataset,\"dataset\" )\n",
    "    fig, ax = plt.subplots()\n",
    "    for model_name in score[dataset].keys():\n",
    "        fpr, tpr, thresolds = metrics.roc_curve(y_true[dataset], score[dataset][model_name])\n",
    "        auc = metrics.auc(fpr, tpr)\n",
    "        print(\"AUC of\",model_name,\"is\",auc)\n",
    "        display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=auc,estimator_name=model_name)\n",
    "        display.plot(ax=ax)\n",
    "        fpr, tpr, auc = 0,0,0 \n",
    "    plt.show()\n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e5dc4c29-bfe9-4d6e-88ac-e370f4543972",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = {} \n",
    "score = {}\n",
    "#cadets\n",
    "y_true[\"cadets\"] = [1,1,1,1,0,0,0,0]\n",
    "score[\"cadets\"] = {}\n",
    "score[\"cadets\"][\"with_ARL\"] = [0.55,0.42,0.57,0.47,0.27,0.36,0,0.25]\n",
    "score[\"cadets\"][\"without_ARL\"] = [0.46,0.11,0.29,0.60,0.46,0.86,0,0.51]\n",
    "\n",
    "#theia\n",
    "y_true[\"theia\"] = [1,1,0,0]\n",
    "score[\"theia\"] = {}\n",
    "score[\"theia\"][\"with_ARL\"] = [0.92,0.59,0,0]\n",
    "score[\"theia\"][\"without_ARL\"] = [0.67,0.37,0,0]\n",
    "\n",
    "#trace\n",
    "y_true[\"trace\"] = [1,1,0,0]\n",
    "score[\"trace\"] = {}\n",
    "score[\"trace\"][\"with_ARL\"] = [0.43,0.46,0.36,0.36]\n",
    "score[\"trace\"][\"without_ARL\"] = [0.62,0.81,0.62,0.68]\n",
    "\n",
    "#OpTC\n",
    "y_true[\"optc\"] = [1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0]\n",
    "score[\"optc\"] = {}\n",
    "score[\"optc\"][\"with_ARL\"] = [0.463,0.48,0.407,0,0,0,0.287,0,0,0.295,0,0,0,0,0,0.308]\n",
    "score[\"optc\"][\"without_ARL\"] = [0.127,0.198,0.32,0,0,0,0.129,0,0,0.174,0,0,0,0,0,0.104]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0f09b12f-2ee5-4937-8594-8757f368ea18",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on cadets dataset\n",
      "AUC of with_ARL is 1.0\n",
      "AUC of without_ARL is 0.40625\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on theia dataset\n",
      "AUC of with_ARL is 1.0\n",
      "AUC of without_ARL is 1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on trace dataset\n",
      "AUC of with_ARL is 1.0\n",
      "AUC of without_ARL is 0.625\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rgduA7cB/CK7r3xtmUCJJ6YdPgyJxmemQej50vzeoGCoSslgSwVHufhtBMhCRkrZ9M3zwN/jiWTiwAZz/BjQ+JdFRSRKJJRE8YmaHAq8B49x9UcgxiSSPbz+At6+BTRnQ4TI4+a9QqWqio5IkE8sMZSdFEsE5wD/NrDpBQtDlIZG9tXU9TLk1eEK4dlO4aAo0yF2BRSQ+YroR2d3XuPsTwGUEzxTcUfA7RCRP7rD4TRh+LHz1KnS+Af40U0lAEqrQHoGZNQcGAGcBmcA4gonsRaQoNq+Bd66Hb96Gw9oEYwGHFVjNXSQuYhkjeIHgw7+7u/8UcjwiZY87LHg5uBS0Yxucehd0vBLKJbrmo0ggljGCjvEIRKRM2rAC3roavp8ODY6HPk9C7caJjkokh3wTgZm94u7nmNlX5HySOKYZykSS2u5d8PlImHo32H5w+iPQ/iLVB5J9UkE9gqsjf/aORyAiZcYv38DEKyHjc2jcDXo/BgfWL/x9IgmS79cTd18deXmFu/8Q/QNcEZ/wREqRXTvgo2Hwz86Q+S30GwnnvaokIPu8WEarugE35VrXM491+7S6O36g/5b/wLgaiQ5Fyqq1S2HdUmjZLygSV7VOoiMSiUlBYwSXE3zzP9LMFkZtqgbMCjuwktZu++ccv+0jWNssuGYrUtIqVoEBL0NzXU2V0qWgHsF/gHeB+4Gbo9Zvdvf1oUYVpiHTgv+wIiICFJwI3N1XmNmfc28ws4NKdTIQEZFshfUIegNzCW4fjZ6ZxoEjQ4xLRETiJN9E4O69I3/GNC2liIiUTrFMXt/JzKpEXp9vZo+aWYPwQxMRkXiI5faZZ4CtZtaGoNjcd8BLoUYlIiJxE0si2OnuDvQFnnL34QS3kIqISBkQywNlm83sFuAPQGcz2w+oEG5YIiISL7H0CAYQTFx/kbuvAeoBw0KNSkRE4qbQRBD58H8ZqGFmvYFt7v5i6JGJiEhcxHLX0DnA58DZBPMWzzaz/mEHJiIi8RHLGMFtwDHu/guAmdUBPgBeCzMwERGJj1jGCPbLSgIRmTG+T0RESoFYegSTzWwKMCayPACYFF5IIiIST7HMWXyjmf0fcEJk1Uh3Hx9uWCIiEi8FzUfQBHgYaAR8Bdzg7qviFZiIiMRHQdf6XwDeBs4iqED6ZFF3bmY9zGypmaWb2c0FtDvLzNzM0op6DBERKZ6CLg1Vc/dnI6+Xmtm8ouzYzMoBwwmmuswAvjCzie6+JFe7asDVwOyi7F9EREpGQYmgspm15X/zEOwfvezuhSWGY4F0d/8ewMzGEtQrWpKr3T3Ag8CNRYxdRERKQEGJYDXwaNTymqhlB04uZN91gR+jljOADtENzKwdUN/d3zGzfBOBmQ0BhgA0aKAK2CIiJamgiWlOCvPAkeJ1jwKDC2vr7iOBkQBpaWkeZlwiIskmzAfDVgH1o5brRdZlqQYcDUw3sxXAccBEDRiLiMRXmIngC6CJmTU0s4rAQGBi1kZ33+Tutd09xd1TgM+APu4+J8SYREQkl9ASgbvvBIYCU4CvgVfcfbGZ3W1mfcI6roiIFE2hTxabmQHnAUe6+92R+YoPdffPC3uvu08iVzkKd78jn7ZdY4pYRERKVCw9gqeBjsCgyPJmgucDRESkDIil6FwHd29nZvMB3H1D5Jq/iIiUAbH0CHZEnhJ2yJ6PYHeoUYmISNzEkgieAMYDB5vZfcDHwN9DjUpEROImljLUL5vZXOAUgvISZ7r716FHJiIicRHLXUMNgK3AW9Hr3H1lmIGJiEh8xDJY/A7B+IABlYGGwFKgZYhxiYhInMRyaahV9HKkUNwVoUUkIiJxVeQniyPlpzsU2lBEREqFWMYIrota3A9oB/wUWkQiIhJXsYwRVIt6vZNgzOD1cMIREZF4KzARRB4kq+buN8QpHhERibN8xwjMrLy77wI6xTEeERGJs4J6BJ8TjAcsMLOJwKvAb1kb3f2NkGMTEZE4iGWMoDKQSTBHcdbzBA4oEYiIlAEFJYKDI3cMLeJ/CSCL5g0WESkjCkoE5YCq5EwAWZQIRETKiIISwWp3vztukYiISEIU9GRxXj0BEREpYwpKBKfELQoREUmYfBOBu6+PZyAiIpIYRS46JyIiZYsSgYhIklMiEBFJckoEIiJJTolARCTJKRGIiCQ5JQIRkSSnRCAikuSUCEREklyoicDMepjZUjNLN7Ob89h+nZktMbOFZjbVzI4IMx4REdlTaIkgMt/xcKAn0AIYZGYtcjWbD6S5e2vgNeChsOIREZG8hdkjOBZId/fv3f13YCzQN7qBu09z962Rxc+AeiHGIyIieQgzEdQFfoxazoisy8/FwLt5bTCzIWY2x8zmrF27tgRDFBGRfWKw2MzOB9KAYXltd/eR7p7m7ml16tSJb3AiImVcLJPX761VQP2o5XqRdTmY2anAbcCJ7r49xHhERCQPYfYIvgCamFlDM6sIDAQmRjcws7bAP4E+7v5LiLGIiEg+QksE7r4TGApMAb4GXnH3xWZ2t5n1iTQbBlQFXjWzBWY2MZ/diYhISMK8NIS7TwIm5Vp3R9TrU8M8voiIFG6fGCwWEZHEUSIQEUlySgQiIklOiUBEJMmFOlgsUpbt2LGDjIwMtm3bluhQRLJVrlyZevXqUaFChZjfo0QgspcyMjKoVq0aKSkpmFmiwxHB3cnMzCQjI4OGDRvG/D5dGhLZS9u2baNWrVpKArLPMDNq1apV5F6qEoFIMSgJyL5mb/5NKhGIiCQ5JQIRkSSnRCBShvXq1YuNGzeyceNGnn766ez106dPp3fv3kXa17p166hQoQIjRozIsT4lJYVWrVrRunVrTjzxRH744YfsbVWrVi10v2+++SZ33313jnWpqakMHDgwx7quXbsyZ86c7OUVK1Zw9NFHZy9//vnndOnShWbNmtG2bVsuueQStm7dSnE89dRTNG7cGDNj3bp1+bYbPXo0TZo0oUmTJowePTp7/dy5c2nVqhWNGzfmqquuwt0BuOGGG/jwww+LFVtJ0l1DIiXgb28tZslPv5boPlscXp07z2hZrH1MmhSU+lqxYgVPP/00V1xxxV7v69VXX+W4445jzJgxXHbZZTm2TZs2jdq1a3PnnXdy77338uyzz8a834ceeoiJE/9Xb/Lrr79m165dzJw5k99++40qVaoUuo+ff/6Zs88+m7Fjx9KxY0cAXnvtNTZv3swBBxwQcyy5derUid69e9O1a9d826xfv56//e1vzJkzBzOjffv29OnTh5o1a3L55Zfz7LPP0qFDB3r16sXkyZPp2bMnV155JZdeeiknn3zyXsdWktQjECmlhg0bxhNPPAHAtddem/2h8uGHH3LeeecBwbf1devWcfPNN/Pdd9+RmprKjTfeCMCWLVvo378/Rx11FOedd172t9X8jBkzhkceeYRVq1aRkZGRZ5uOHTuyatUe047ka9myZVSqVInatWvnOM4f/vAHTjvtNCZMmBDTfoYPH86FF16YnQQA+vfvzyGHHBJzLHlp27YtKSkpBbaZMmUK3bp146CDDqJmzZp069aNyZMns3r1an799VeOO+44zIwLLriAN998E4AjjjiCzMxM1qxZU6z4Sop6BCIloLjf3PdG586deeSRR7jqqquYM2cO27dvZ8eOHcycOZMuXbrkaPvAAw+waNEiFixYAASXhubPn8/ixYs5/PDD6dSpE7NmzeKEE07I81g//vgjq1ev5thjj+Wcc85h3LhxXH/99Xu0mzx5MmeeeWbM5zBr1izatWuXY924ceN4//33+eabb3jyySc599xzC93PokWLuPDCCwttt3TpUgYMGJDntunTp3PggQfGFHe0VatWUb/+/+bgqlevHqtWrWLVqlXUq1dvj/VZ2rVrx6xZszjrrLOKfMySpkQgUkq1b9+euXPn8uuvv1KpUiXatWvHnDlzmDlzZnZPoSDHHnts9gdVamoqK1asyDcRjBs3jnPOOQeAgQMHctFFF+VIBCeddBLr16+natWq3HPPPTGfw+rVq4mefnbOnDnUrl2bBg0aULduXS666CLWr1/PQQcdlOdtkUW9VbJZs2bZyTDRDj74YH766adEhwHo0pBIqVWhQgUaNmzIqFGjOP744+ncuTPTpk0jPT2d5s2bF/r+SpUqZb8uV64cO3fuzLftmDFjGDVqFCkpKfTp04eFCxfy7bffZm+fNm0aP/zwA6mpqdx5550xn8P++++f4+GnMWPG8M0335CSkkKjRo349ddfef311wGoVasWGzZsyG67fv367EtKLVu2ZO7cuYUeb+nSpaSmpub5s3Hjxpjjjla3bl1+/PHH7OWMjAzq1q1L3bp1c1xCy1qfZdu2bey///57dcySpkQgUop17tyZhx9+mC5dutC5c2dGjBhB27Zt9/imXK1aNTZv3rxXx1i2bBlbtmxh1apVrFixghUrVnDLLbcwZsyYHO3Kly/PP/7xD1588UXWr18f076bN29Oeno6ALt37+aVV17hq6++yj7OhAkTso/TtWtX/v3vf2ePZYwePZqTTjoJgKFDhzJ69Ghmz56dve833niDn3/+OcfxsnoEef3szWUhgO7du/Pee++xYcMGNmzYwHvvvUf37t057LDDqF69Op999hnuzosvvkjfvn2z37ds2bIcdz0lkhKBSCnWuXNnVq9eTceOHTnkkEOoXLkynTt33qNdrVq16NSpE0cffXT2YHGsxowZQ79+/XKsO+uss/ZIBACHHXYYgwYNYvjw4QBs3bqVevXqZf88+uijOdp36dKF+fPn4+7MnDmTunXrcvjhh+fYvmTJElavXs2QIUOoVq0abdq0oU2bNmzZsoUbbrgBgEMOOYSxY8dyww030KxZM5o3b86UKVOoVq1akc41tyeeeIJ69eqRkZFB69atueSSS4DgElbW64MOOoi//vWvHHPMMRxzzDHccccdHHTQQQA8/fTTXHLJJTRu3JhGjRrRs2dPIChYmJ6eTlpaWrHiKylW2J0C+5q0tDSPvpc4Vv9+5DrO3/w83PoTVCz8djSRwnz99dcxXYKRgl199dWcccYZnHpq8sxcO378eObNm1ek8ZSiyOvfppnNdfc8M496BCKSULfeemuxH/wqbXbu3JnnXVeJoruGRCRbv379WL58eY51Dz74IN27dw/tmIcccgh9+vQJbf/7orPPPjvRIeSgRCAi2caPH5/oECQBdGlIRCTJKRGIiCQ5JQIRkSSnRCAikuSUCETKsJKcjyA/06dP55NPPompbV7zDAwePJiGDRuSmppKmzZtmDp1ava23HMQ5GX16tV7nMs111xD3bp12b17d/a6u+66i4cffjhHu6zqrABr1qxh4MCBNGrUiPbt29OrVy+WLVsW03nlZ/v27QwYMIDGjRvToUMHVqxYkWe7jRs3ZleCbd68OZ9++ikAN954I0cddRStW7emX79+2WUwvvrqKwYPHlys2KLpriGRkvDuzbDmq5Ld56GtoOcDxdpFSc5HkJ/p06dTtWpVjj/++ALbFTTPwLBhw+jfvz/Tpk1jyJAhOeoYFebRRx/l0ksvzV7evXs348ePp379+nz00UfZZSgK4u7069ePCy+8kLFjxwLw5Zdf8vPPP9O0adOYY8nt+eefp2bNmqSnpzN27Fhuuukmxo0bt0e7q6++mh49evDaa6/x+++/Zz9X0a1bN+6//37Kly/PTTfdxP3338+DDz5Iq1atyMjIYOXKlTRo0GCv48uiHoFIKRXWfARTp06lbdu2tGrViosuuojt27fn2BcEJRa6du3KihUrGDFiBI899hipqanMnDkz33hjmWegqPMZALz++uv06NEje3n69Om0bNmSyy+/PM8yGHmZNm0aFSpUyDHhTps2bfIs11EUEyZMyC6P3b9/f6ZOnbrHvA+bNm1ixowZXHzxxQBUrFgxu+7RaaedRvnywff14447LkcRuzPOOCM7aRWXegQiJaGY39z3RhjzEaSlpTF48GCmTp1K06ZNueCCC3jmmWe45ppr8owhJSWFyy67jKpVq2bX/clPLPMMFHU+g+XLl1OzZs0clVTHjBnDoEGD6Nu3L7feeis7duygQoUKBe5n0aJFtG/fPqZjdu7cOc8Cfg8//PAeZTKi5yooX748NWrUIDMzM8dEPMuXL6dOnTr88Y9/5Msvv6R9+/Y8/vjje8zM9sILL+SYSyEtLY0HHniAv/zlLzHFXRD1CERKqdzzEXTs2DF7PoJYvslmzUew3377Zc9HsHTpUho2bJh9OeTCCy9kxowZxY41ep6BU045hfnz5+eoUHrjjTfStGlTzj33XG666aaY95t7PoPff/+dSZMmceaZZ1K9enU6dOjAlClTgPznLijqnAYzZ87Ms3rp3tZK2rlzJ/PmzePyyy9n/vz5VKlShQceyPnF4r777qN8+fLZPT0o2fkMQk0EZtbDzJaaWbqZ3ZzH9kpmNi6yfbaZpYQZj0hZEs/5CCD4Rps1+Bo9h0AsCppnAILLXMuWLePBBx/koosuinm/ueczmDJlChs3bqRVq1akpKTw8ccfZ18eyj2fAcDmzZs58MADY57PAIIeQV7zGXzwwQd7tI2eq2Dnzp1s2rSJWrVq5WiTVZm1Q4cOQHAJad68ednbR40axdtvv83LL7+cI2mV5HwGoSUCMysHDAd6Ai2AQWbWIlezi4EN7t4YeAx4MKx4RMqikp6PoFmzZqxYsSJ7joCXXnqJE088EQguA2V9WEZ/iBe278LmGYg2dOhQdu/enf0tvjBNmzbNcSfOmDFjeO6557KPs3z5ct5//322bt1Kly5dmDhxYnasb7zxBm3atKFcuXKcfPLJbN++nZEjR2bva+HChXmOeRSlR9CnTx9Gjx4NwGuvvcbJJ5+8x+/m0EMPpX79+ixduhQIxmhatAg+KidPnsxDDz3ExIkTOeCAA3K8ryTnMwizR3AskO7u37v778BYoG+uNn2B0ZHXrwGnWFH7aSJJrKTnI6hcuTL/+te/OPvss2nVqhX77bdf9gDqnXfeydVXX01aWhrlypXLfs8ZZ5zB+PHj8x0sLmyegWhmxu23385DDz2Uve7000/P/tacu1hblSpVaNSoEenp6WzdupXJkydz+umn59h+wgkn8NZbb9G6dWuGDh3KCSecQGpqKiNGjOC5557LPu748eP54IMPaNSoES1btuSWW27h0EMPzffvKhYXX3wxmZmZNG7cmEcffTT7ks9PP/1Er169sts9+eSTnHfeebRu3ZoFCxZw6623AkFi3Lx5M926dSM1NTXHYPa0adNynGtxhDYfgZn1B3q4+yWR5T8AHdx9aFSbRZE2GZHl7yJt1uXa1xBgCECDBg3a//DDD0WOZ+xLz9Aqcwot/zwWKlTe29MSyab5CPYN48ePZ+7cudx7772JDiVutm/fzoknnsjHH3+cfVdRtKLOR1Aq7hpy95HASAgmptmbfQz8w+XA5SUZlojsA/r160dmZmaiw4irlStX8sADD+SZBPZGmIlgFVA/arleZF1ebTLMrDxQA0iu36hIGXLffffx6quv5lh39tlnc9ttt4V63KxpI5NFkyZNaNKkSYntL8xE8AXQxMwaEnzgDwRy3zg8EbgQ+BToD3zopW3uTElq7l7k2w/Lsttuuy30D30p2N58hIY2WOzuO4GhwBTga+AVd19sZnebWdZ0RM8DtcwsHbgO2OMWU5F9VeXKlcnMzNyr/3giYXB3MjMzqVy5aOOgSTN5vUhJ27FjBxkZGUW+p14kTJUrV6ZevXp7PE1d6geLRfZFWQ90iZR2KjEhIpLklAhERJKcEoGISJIrdYPFZrYWKPqjxYHawLpCW5UtOufkoHNODsU55yPcvU5eG0pdIigOM5uT36h5WaVzTg465+QQ1jnr0pCISJJTIhARSXLJlghGFt6kzNE5Jwedc3II5ZyTaoxARET2lGw9AhERyUWJQEQkyZXJRGBmPcxsqZmlm9keFU3NrJKZjYtsn21mKQkIs0TFcM7XmdkSM1toZlPN7IhExFmSCjvnqHZnmZmbWam/1TCWczazcyK/68Vm9p94x1jSYvi33cDMppnZ/Mi/71557ae0MLMXzOyXyAyOeW03M3si8vex0MzaFfug7l6mfoBywHfAkUBF4EugRa42VwAjIq8HAuMSHXcczvkk4IDI68uT4Zwj7aoBM4DPgLRExx2H33MTYD5QM7J8cKLjjsM5jwQuj7xuAaxIdNzFPOcuQDtgUT7bewHvAgYcB8wu7jHLYo/gWCDd3b9399+BsUDfXG36AqMjr18DTrHSPbtIoefs7tPcfWtk8TOCGeNKs1h+zwD3AA8CZaFWdCznfCkw3N03ALj7L3GOsaTFcs4OVI+8rgH8FMf4Spy7zwDWF9CkL/CiBz4DDjSzw4pzzLKYCOoCP0YtZ0TW5dnGgwl0NgG14hJdOGI552gXE3yjKM0KPedIl7m+u78Tz8BCFMvvuSnQ1MxmmdlnZtYjbtGFI5Zzvgs438wygEnAlfEJLWGK+v+9UJqPIMmY2flAGnBiomMJk5ntBzwKDE5wKPFWnuDyUFeCXt8MM2vl7hsTGVTIBgGj3P0RM+sIvGRmR7v77kQHVlqUxR7BKqB+1HK9yLo825hZeYLuZGZcogtHLOeMmZ0K3Ab0cfftcYotLIWdczXgaGC6ma0guJY6sZQPGMfye84AJrr7DndfDiwjSAylVSznfDHwCoC7fwpUJijOVlbF9P+9KMpiIvgCaGJmDc2sIsFg8MRcbSYCF0Ze9wc+9MgoTClV6DmbWVvgnwRJoLRfN4ZCztndN7l7bXdPcfcUgnGRPu5emuc5jeXf9psEvQHMrDbBpaLv4xhjSYvlnFcCpwCYWXOCRLA2rlHG10TggsjdQ8cBm9x9dXF2WOYuDbn7TjMbCkwhuOPgBXdfbGZ3A3PcfSLwPEH3MZ1gUGZg4iIuvhjPeRhQFXg1Mi6+0t37JCzoYorxnMuUGM95CnCamS0BdgE3unup7e3GeM7XA8+a2bUEA8eDS/MXOzMbQ5DMa0fGPe4EKgC4+wiCcZBeQDqwFfhjsY9Ziv++RESkBJTFS0MiIlIESgQiIklOiUBEJMkpEYiIJDklAhGRJKdEIPskM9tlZguiflIKaLulBI43ysyWR441L/KEalH38ZyZtYi8vjXXtk+KG2NkP1l/L4vM7C0zO7CQ9qmlvRqnhE+3j8o+ycy2uHvVkm5bwD5GAW+7+2tmdhrwsLu3Lsb+ih1TYfs1s9HAMne/r4D2gwmqrg4t6Vik7FCPQEoFM6samUdhnpl9ZWZ7VBo1s8PMbEbUN+bOkfWnmdmnkfe+amaFfUDPABpH3ntdZF+LzOyayLoqZvaOmX0ZWT8gsn66maWZ2QPA/pE4Xo5s2xL5c6yZnR4V8ygz629m5cxsmJl9Eakx/6cY/lo+JVJszMyOjZzjfDP7xMyaRZ7EvRsYEIllQCT2F8zs80jbvCq2SrJJdO1t/egnrx+Cp2IXRH7GEzwFXz2yrTbBU5VZPdotkT+vB26LvC5HUG+oNsEHe5XI+puAO/I43iigf+T12cBsoD3wFVCF4KnsxUBb4Czg2aj31oj8OZ3InAdZMUW1yYqxHzA68roiQRXJ/YEhwO2R9ZWAOUDDPOLcEnV+rwI9IsvVgfKR16cCr0deDwaeinr/34HzI68PJKhFVCXRv2/9JPanzJWYkDLjv+6emrVgZhWAv5tZF2A3wTfhQ4A1Ue/5Angh0vZNd19gZicSTFYyK1JaoyLBN+m8DDOz2wnq1FxMUL9mvLv/FonhDaAzMBl4xMweJLicNLMI5/Uu8LiZVQJ6ADPc/b+Ry1Gtzax/pF0NgmJxy3O9f38zWxA5/6+B96PajzazJgRlFirkc/zTgD5mdkNkuTLQILIvSVJKBFJanAfUAdq7+w4LKopWjm7g7jMiieJ0YJSZPQpsAN5390ExHONGd38ta8HMTsmrkbsvs2Cug17AvWY21d3vjuUk3H2bmU0HugMDCCZagWC2qSvdfUohu/ivu6ea2QEE9Xf+DDxBMAHPNHfvFxlYn57P+w04y92XxhKvJAeNEUhpUQP4JZIETgL2mHPZgnmYf3b3Z4HnCKb7+wzoZGZZ1/yrmFnTGI85EzjTzA4wsyoEl3VmmtnhwFZ3/zdBMb+85ozdEemZ5GUcQaGwrN4FBB/ql2e9x8yaRo6ZJw9mm7sKuN7+V0o9qxTx4KimmwkukWWZAlxpke6RBVVpJckpEUhp8TKQZmZfARcA3+TRpivwpZnNJ/i2/bi7ryX4YBxjZgsJLgsdFcsB3X0ewdjB5wRjBs+5+3ygFfB55BLNncC9ebx9JLAwa7A4l/cIJgb6wIPpFyFIXEuAeRZMWv5PCumxR2JZSDAxy0PA/ZFzj37fNKBF1mAxQc+hQiS2xZFlSXK6fVREJMmpRyAikuSUCEREkpwSgYhIklMiEBFJckoEIiJJTolARCTJKRGIiCS5/wcqXIVEhr5E4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on optc dataset\n",
      "AUC of with_ARL is 0.84375\n",
      "AUC of without_ARL is 0.8020833333333333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dataset in score.keys():\n",
    "    print(\"Evaluation on\",dataset,\"dataset\" )\n",
    "    fig, ax = plt.subplots()\n",
    "    for model_name in score[dataset].keys():\n",
    "        fpr, tpr, thresolds = metrics.roc_curve(y_true[dataset], score[dataset][model_name])\n",
    "        auc = metrics.auc(fpr, tpr)\n",
    "        print(\"AUC of\",model_name,\"is\",auc)\n",
    "        display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=auc,estimator_name=model_name)\n",
    "        display.plot(ax=ax)\n",
    "        fpr, tpr, auc = 0,0,0 \n",
    "    plt.show()\n",
    "    plt.clf()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "envmegrapt",
   "language": "python",
   "name": "envmegrapt"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
